<html><head><title>Data Scientist for Materials Science - Golden, CO 80401</title></head>
<body><h2>Data Scientist for Materials Science - Golden, CO 80401</h2>
<p><b>Posting Title
</b></p>Data Scientist for Materials Science
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</p><p><b>Location
</b></p>CO - Golden
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</p><p><b>Position Type
</b></p>Limited Term (Fixed Term)
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</p><p><b>Hours Per Week
</b></p>40
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</p><h2 class="jobSectionHeader"><b>Job Description
</b></h2><p>The Data, Analysis and Visualization Group in the NREL Computational Science Center has an opening for a full-time researcher in applied data science methods for materials with an emphasis in machine learning techniques for predictive analytics.
</p><p></p><p>Predictive analytics problems in materials science focus on highly distributed, scalable approaches that can make use of large, complex theoretical and experimental materials databases. Specific areas of interest include: complex feature engineering, property prediction, candidate generation, interpretable AI, and structural decomposition.
</p><p>NREL is looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL. The successful candidate will collaborate with NREL staff and researchers, other national labs and universities on efforts to develop data science solutions at scale to real-world problems in renewable energy research. In addition to existing skills, candidates should demonstrate a high degree of curiosity, willingness to learn new skills and ability to adapt to the data needs of differing domains.
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</p><h2 class="jobSectionHeader"><b>Basic Qualifications
</b></h2>Master's Degree in Computer Science, Applied Mathematics, Statistics or related. Or, Bachelor's Degree in Computer Science, Applied Mathematics, Statistics or related and 2 or more years of experience .
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</p><h2 class="jobSectionHeader"><b>Additional Required Qualifications
</b></h2><p></p><p><b>Preferred Qualifications
</b></p><p>Preferred Qualifications:
</p><ul><li>A strong familiarity with Unix/Linux operating systems and open source software. Preferably in a production data-center cloud or HPC environment</li><li>Demonstrated experience with machine learning and applied predictive modeling, applied statistical analysis</li><li>Familiarity with foundational statistical concepts such as regression models, uncertainty quantification, Bayesian analysis, model selection, clustering, outlier detection, etc</li><li>Experience in big data analytics on diverse, asynchronous data sets including experience in designing efficient and robust ETL workflows. Experience accessing and designing APIs and databases (SQL)</li><li>Sufficient software engineering expertise to enable production-quality solutions: object-oriented design, coding and testing patterns; engineering software platforms and large-scale data infrastructures. Interested candidates should have experience programming Python and R (optionally, in addition to other languages)</li><li>Experience using deep learning frameworks (e.g., TensorFlow) on high performance computing/GPU platforms</li><li>Experience with parallel and distributed programming, big data frameworks, notably including streaming data systems (e.g., Kafka), and scientific plotting libraries (e.g., plot.ly, matplotlib, bokeh, ggplot)</li><li>Background in relevant engineering disciplines especially: chemistry, physics, and materials science</li><li>Candidates should be able to demonstrate some existing skills and experience in applying data science in industry, academia, or government settings</li></ul><p>.
</p><p><b>Submission Guidelines
</b></p><p>Please note that in order to be considered an applicant for any position at NREL you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
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</p><p><b>EEO Policy
</b></p><p>NREL is dedicated to the principles of equal employment opportunity. NREL promotes a work environment that does not discriminate against workers or job applicants and prohibits unlawful discrimination on the basis of race, color, religion, sex, national origin, disability, age, marital status, ancestry, actual or perceived sexual orientation, gender identity, or veteran status, including special disabled veterans.
</p><p></p><p>NREL validates right to work using E-Verify. NREL will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee’s Form I-9 to confirm work authorization.</p></body>
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